from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2021-01-03 14:10:18.964533
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sun, 03, Jan, 2021
Time: 14:10:23
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -44.5339
Nobs: 160.000 HQIC: -45.5613
Log likelihood: 1747.82 FPE: 8.10165e-21
AIC: -46.2637 Det(Omega_mle): 4.69476e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.459408 0.157384 2.919 0.004
L1.Burgenland 0.137845 0.080004 1.723 0.085
L1.Kärnten -0.236123 0.064444 -3.664 0.000
L1.Niederösterreich 0.111123 0.186546 0.596 0.551
L1.Oberösterreich 0.253124 0.159523 1.587 0.113
L1.Salzburg 0.174361 0.082475 2.114 0.035
L1.Steiermark 0.081330 0.114837 0.708 0.479
L1.Tirol 0.149403 0.076732 1.947 0.052
L1.Vorarlberg 0.006754 0.073334 0.092 0.927
L1.Wien -0.120346 0.154358 -0.780 0.436
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.513790 0.203599 2.524 0.012
L1.Burgenland 0.011911 0.103497 0.115 0.908
L1.Kärnten 0.367416 0.083367 4.407 0.000
L1.Niederösterreich 0.134350 0.241323 0.557 0.578
L1.Oberösterreich -0.187507 0.206366 -0.909 0.364
L1.Salzburg 0.186838 0.106693 1.751 0.080
L1.Steiermark 0.251683 0.148558 1.694 0.090
L1.Tirol 0.142990 0.099263 1.441 0.150
L1.Vorarlberg 0.177304 0.094867 1.869 0.062
L1.Wien -0.583079 0.199683 -2.920 0.004
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.294656 0.068598 4.295 0.000
L1.Burgenland 0.105615 0.034871 3.029 0.002
L1.Kärnten -0.026464 0.028089 -0.942 0.346
L1.Niederösterreich 0.069390 0.081308 0.853 0.393
L1.Oberösterreich 0.290165 0.069530 4.173 0.000
L1.Salzburg -0.001671 0.035948 -0.046 0.963
L1.Steiermark -0.021075 0.050053 -0.421 0.674
L1.Tirol 0.088691 0.033445 2.652 0.008
L1.Vorarlberg 0.126297 0.031963 3.951 0.000
L1.Wien 0.080890 0.067279 1.202 0.229
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.204051 0.079413 2.569 0.010
L1.Burgenland -0.012493 0.040369 -0.309 0.757
L1.Kärnten 0.022474 0.032517 0.691 0.489
L1.Niederösterreich 0.025681 0.094127 0.273 0.785
L1.Oberösterreich 0.412964 0.080492 5.130 0.000
L1.Salzburg 0.096805 0.041615 2.326 0.020
L1.Steiermark 0.182414 0.057945 3.148 0.002
L1.Tirol 0.032888 0.038717 0.849 0.396
L1.Vorarlberg 0.095952 0.037003 2.593 0.010
L1.Wien -0.062163 0.077886 -0.798 0.425
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.594069 0.165604 3.587 0.000
L1.Burgenland 0.074471 0.084183 0.885 0.376
L1.Kärnten 0.003172 0.067810 0.047 0.963
L1.Niederösterreich -0.039538 0.196289 -0.201 0.840
L1.Oberösterreich 0.154026 0.167855 0.918 0.359
L1.Salzburg 0.052740 0.086782 0.608 0.543
L1.Steiermark 0.113435 0.120835 0.939 0.348
L1.Tirol 0.210926 0.080739 2.612 0.009
L1.Vorarlberg 0.004339 0.077164 0.056 0.955
L1.Wien -0.149597 0.162420 -0.921 0.357
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.159117 0.115277 1.380 0.167
L1.Burgenland -0.026525 0.058600 -0.453 0.651
L1.Kärnten -0.013743 0.047203 -0.291 0.771
L1.Niederösterreich 0.173898 0.136637 1.273 0.203
L1.Oberösterreich 0.395384 0.116844 3.384 0.001
L1.Salzburg -0.026931 0.060409 -0.446 0.656
L1.Steiermark -0.046407 0.084113 -0.552 0.581
L1.Tirol 0.189106 0.056203 3.365 0.001
L1.Vorarlberg 0.039717 0.053714 0.739 0.460
L1.Wien 0.164595 0.113060 1.456 0.145
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.240444 0.144836 1.660 0.097
L1.Burgenland 0.062737 0.073625 0.852 0.394
L1.Kärnten -0.048032 0.059306 -0.810 0.418
L1.Niederösterreich -0.037996 0.171672 -0.221 0.825
L1.Oberösterreich -0.101904 0.146804 -0.694 0.488
L1.Salzburg 0.013292 0.075899 0.175 0.861
L1.Steiermark 0.381525 0.105681 3.610 0.000
L1.Tirol 0.518065 0.070614 7.337 0.000
L1.Vorarlberg 0.195552 0.067486 2.898 0.004
L1.Wien -0.220274 0.142050 -1.551 0.121
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.122382 0.169573 0.722 0.470
L1.Burgenland 0.013756 0.086200 0.160 0.873
L1.Kärnten -0.114151 0.069435 -1.644 0.100
L1.Niederösterreich 0.215389 0.200992 1.072 0.284
L1.Oberösterreich 0.013474 0.171877 0.078 0.938
L1.Salzburg 0.221919 0.088862 2.497 0.013
L1.Steiermark 0.143249 0.123730 1.158 0.247
L1.Tirol 0.092533 0.082674 1.119 0.263
L1.Vorarlberg 0.014448 0.079013 0.183 0.855
L1.Wien 0.287928 0.166311 1.731 0.083
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.587785 0.093221 6.305 0.000
L1.Burgenland -0.020889 0.047387 -0.441 0.659
L1.Kärnten 0.000546 0.038171 0.014 0.989
L1.Niederösterreich -0.009911 0.110493 -0.090 0.929
L1.Oberösterreich 0.280838 0.094487 2.972 0.003
L1.Salzburg 0.011498 0.048851 0.235 0.814
L1.Steiermark 0.000576 0.068019 0.008 0.993
L1.Tirol 0.077168 0.045449 1.698 0.090
L1.Vorarlberg 0.169141 0.043436 3.894 0.000
L1.Wien -0.091901 0.091428 -1.005 0.315
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.139633 -0.003106 0.205799 0.244077 0.059038 0.099201 -0.082819 0.161681
Kärnten 0.139633 1.000000 -0.005978 0.186236 0.130407 -0.145555 0.171970 0.030784 0.297322
Niederösterreich -0.003106 -0.005978 1.000000 0.262368 0.079434 0.203093 0.107037 0.043005 0.354259
Oberösterreich 0.205799 0.186236 0.262368 1.000000 0.275909 0.291308 0.107769 0.075875 0.108719
Salzburg 0.244077 0.130407 0.079434 0.275909 1.000000 0.146419 0.070211 0.080407 -0.022837
Steiermark 0.059038 -0.145555 0.203093 0.291308 0.146419 1.000000 0.105642 0.087033 -0.128934
Tirol 0.099201 0.171970 0.107037 0.107769 0.070211 0.105642 1.000000 0.150533 0.140019
Vorarlberg -0.082819 0.030784 0.043005 0.075875 0.080407 0.087033 0.150533 1.000000 0.102998
Wien 0.161681 0.297322 0.354259 0.108719 -0.022837 -0.128934 0.140019 0.102998 1.000000